extfieldsofstudy:
Biology
Computer Science
Medicine

text:
MCM-test: a fuzzy-set-theory-based approach to differential analysis of gene pathways

Background Gene pathway can be defined as a group of genes that interact with each other to perform some biological processes. Along with the efforts to identify the individual genes that play vital roles in a particular disease, there is a growing interest in identifying the roles of gene pathways in such diseases. Results This paper proposes an innovative fuzzy-set-theory-based approach, Multi-dimensional Cluster Misclassification test (MCM-test), to measure the significance of gene pathways in a particular disease. Experiments have been conducted on both synthetic data and real world data. Results on published diabetes gene expression dataset and a list of predefined pathways from KEGG identified OXPHOS pathway involved in oxidative phosphorylation in mitochondria and other mitochondrial related pathways to be deregulated in diabetes patients. Our results support the previously supported notion that mitochondrial dysfunction is an important event in insulin resistance and type-2 diabetes. Conclusion Our experiments results suggest that MCM-test can be successfully used in pathway level differential analysis of gene expression datasets. This approach also provides a new solution to the general problem of measuring the difference between two groups of data, which is one of the most essential problems in most areas of research.

Background
Current microarray technologies conduct simultaneous studies of gene expression data of thousands of genes under different conditions. In most of these studies, expression data are analyzed using various standard statistical methods to identify a list of genes responsible for a particular condition. However, in these approaches, interplay among genes is not taken into account. Since organisms behave as complex systems, functioning through chemical networks and interaction of various molecules (also known as pathways), this interplay of genes may provide invaluable insight to the understanding of various from Symposium of Computations in Bioinformatics and Bioscience (SCBB07) Iowa City, Iowa, USA. 13-15 August 2007 diseases. Thus, along with the efforts to identify the individual genes that play vital roles in a particular disease, there is a growing interest in identifying the roles of different pathways in such diseases.
Biological pathway is a group of related genes coding for proteins that interact with each other to perform some biological processes. According to the biological processes they are involved with, pathways can be divided into several categories, such as metabolic pathways and regulatory pathways. Metabolic pathways are series of chemical reactions occurring within a cell, catalyzed by enzymes, resulting in either the formation of a metabolic product to be used or stored by the cell, or the initiation of another metabolic pathway. Regulatory pathways represent proteinprotein interactions.
During the past few years, many signaling and metabolic pathways have been discovered experimentally and have been integrated into pathway databases, such as KEGG [1] and Biocarta [2]. Various statistical techniques have been developed to analyze microarray expression data for the relevance of predefined pathways to a particular disease. These techniques include gene set enrichment analysis [3,4], pathway level analysis of gene expression using singular value decomposition by Tomfohr et al. [5], and hypothesis testing [6] by Tian et al. These approaches are reviewed in detail in the related works section.
Generally speaking, these approaches can be divided into two categories: • Conduct statistical differential analysis at the individual gene level, and integrate the result statistics of the genes in the same pathway; • Obtain activity level indices of each pathway for different sample groups and conduct differential analysis of these indices.
For the first category, when the statistics at individual gene level miss significant genes, the effectiveness of the pathway analysis will be affected. An example is given in the later part of this section. For the second approach, extracting pathway activity level indices from gene expression data may cause loss of information.
Diabetes is a group of diseases characterized by high levels of blood glucose resulting from defects in insulin production, insulin action, or both. It is one of the most common diseases, affecting 18.2 million people in the United States, or 6.3% of the population [7]. Hence, identifying active pathways in diabetes is a critical task for understanding this disease. Several pathway analysis works have been proposed in this area [3,5,6].
In gene set enrichment analysis (GSEA) [3], a differential statistic is calculated first for each gene from their expression data of two different groups of samples. Then the genes are ordered according to the statistic values. A running sum of weights is calculated from the ordered list for a particular pathway. The maximum value of this running sum is called the enrichment score of that pathway. To measure the significance of this score, a null distribution of enrichment scores is generated by permuting the sample labels. This approach falls into the first category stated previously, i.e., statistical analysis at individual gene level is performed followed by an integration of these statistics of genes in the same pathway.
In [5], a hypothesis testing framework for pathway differential analysis is proposed. T-test and Wilcoxon rank test are recommended to measure the difference of expressions of a single gene between two groups of samples. Then this statistic is accumulated over each gene in a particular pathway and standardized by the total number of genes in this pathway. The significance of the result is then interpreted by rejecting two null hypotheses, each with a null population generated by permuting sample labels or gene indices. This approach also belongs to the first category above. Statistical analysis at individual gene level is still required for the pathway analysis in this approach.
In [6], singular value decomposition is used to obtain pathway activity levels from the gene expression matrix. Ttest is applied to the pathway activity levels of the two different sample groups to measure the difference. Significance of the measurement is also obtained by permuting the sample labels. In this approach, no differential analysis at individual gene level is required. However, an extraction of pathway activity level prior to the differential analysis is required. During this extraction process, since only the first eigenvector of singular value decomposition is used, some information of expressions is lost. This approach belongs to the second category stated above.
As discussed above, either t-test or rank sum test is used as a core step by [3,6] to identify individual genes which are expressed differently from two different sample groups. Thus these methods inevitably inherit the disadvantage of t-test and rank sum test. While the t-test is very sensitive to extreme values and cannot distinguish two sets with close means even though the two sets are significantly different from each others, the rank sum test is not sensitive to absolute values. In turn, those pathways contain genes which can not be identified by t-test or rank sum test but actually are significantly differently expressed in two different sample groups will be affected. For example, as showed in Table 1, the expressions of Gene 3 are significantly different under two conditions. However this gene was not identified by t-test. Thus, a pathway involving this gene is less likely to be identified by the first category of analysis that uses t-test at the gene level.
In this paper, we propose an innovative fuzzy-set-theorybased approach for differential analysis of gene pathways and apply it on identifying significant pathways for diabetes. In our proposed MCM-test, instead of identifying individual genes first, the differential analysis is done directly at the pathway level without individual gene differential statistic. All expression values of genes which belong to a pathway of a particular patient are treated as a vector. The intuition behind this is based on the fact that genes for each patient interplay with each other. MCMtest does not extract activity level of pathways either. This allows keeping the maximum amount of information for the pathway differential analysis. Moreover, the fuzzy concept makes the approach more tolerant to individual data item noise.

Results
To investigate our approach, we conducted experiments on both synthetic data and real world data. We first conducted a series of experiments on synthetic datasets to find the characteristics of MCM d-value. We then used the MCM-test on the real world diabetes dataset analyzed by Tomfohr et al. [5] and GSEA [3]. Results on real world diabetes data identified several pathways that were deregulated in diabetes patients. The top three pathways identified were related to mitochondrial functions in accordance with previous diabetes studies. Mitochondrial dysfunction is known to be related to insulin resistance and type-2 diabetes. Our data suggests that the method can be successfully used in pathway level differential analysis of gene expression datasets.

Relationship between MCM d-value and mean difference of the distributions
Suppose two sets S 1 and S 2 are drawn from two different distributions, then a good divergence value will satisfy the following property: the less the overlap, the higher the dvalue. To validate that our MCM-test has this property, we performed the following steps: 1. generated 17 values from Gaussian distribution N (μ, σ), where μ is the mean and σ is the variance, to use as gene expression data. The number 17 was chosen to mimic the real world diabetes dataset used for the analysis in this paper.

repeated
Step 1 for 100 times to get expression data of 100 genes 3. generated 17 values from Gaussian distribution N (μ + x, σ), with x = 0 at this time.  Two datasets are generated from two distributions N (μ, σ) and N (μ + x, σ). As the mean difference, x, increases, the dvalue also increases. We also note that the error rate becomes stable after the size of the population becomes greater than 8000.

Relationship between MCM d-value and empirical p-value
Suppose two vectors S 1 and S 2 are drawn from same Normal distribution. What is the probability that the MCM dvalue of these vectors is greater than a particular D? Does the probability increases with the increase of D? To answer these questions, we studied the relationship between MCM d-value and empirical p-value as follows: 1. We generated 15000 pairs of sets, each set with 15 values from standard normal distribution.
2. From these 15000 pairs of sets, we randomly selected 100 pairs of sets to simulate expression data of a pathway with 100 genes under two conditions. We calculated dvalue for this pathway. Since we know that the data size required to obtain stable standard deviation of d-value is 8000 from the previous experiment, this process is repeated 10000 times.
3. For each pathway generated above with d-value D, we calculated the empirical p-value as n+1/10001, where n is the number of d-values generated above that are equal to or greater than D. The relationship between the d-value and p-value is shown in Figure 3.
In Figure 3 we can see that as the d-value increases, the pvalue decreases. In particular, when d-value is greater than 0.809, we have p-value ≤ 0.05.

Impact of number of samples on error rate of MCM-test dvalue
In order to understand the effect of the number of samples on error rate of MCM d-value, we generated datasets with different sample sizes. For each sample size, we generated 10000 datasets and calculated the corresponding 10000 d-values. The standard deviation of these d-values was calculated. This process is repeated 10 times and the average of the standard deviations is recorded as the error rate. The same is done for the other sample sizes. The relationship between number of samples and the error rate is shown in Relationship between MCM d-value and its empirical p-value Figure 3 Relationship between MCM d-value and its empirical p-value. As the d-value increases, the corresponding empirical p-value decreases.
Impact of number of permutation on the error rate of PDF of MCM d-value

Analyzing the diabetes dataset with MCM-test
The diabetes dataset contains the transcriptional profiles of smooth muscle biopsies of diabetic and normal individuals. In the expression dataset, for each gene, there are 17 expression values from subjects with type 2 diabetes (DM2), 17 expression values from subjects with normal glucose tolerance (NGT) and 10 expression values from subjects with impaired glucose tolerance (IGT). For our analysis, we only used the 34 expression values from subjects with type 2 diabetes and subjects with normal glucose tolerance. Furthermore, we used about 150 pathways obtained from KEGG (Kyoto Encyclopedia of Genes and Genomes) [1].
The expression values in the dataset which are too small, i.e., less than 100 are considered to be the result of noise. So, to minimize the effect of these low values, we only included the genes which have at least one of the expression values greater than 100. Out of the 22,283 genes in the dataset, 10,983 met the filtering criteria. The d-value for each pathway was calculated as described in the methodology section before. The p-value for the pathway was calculated using permutation test. We permuted the genes 1000 times, each time selecting the same number of genes as that of the pathway under consideration. We then calculated the d-value of each pathway obtained thus and the p-value for the pathway was the fraction of times the dvalues of the pathways obtained by 1000 permutation equaled or exceeded the original d-value.
The pathways are ordered in the ascending order of their p-values. The significant pathways, i.e., the pathways with p-value less than 0.05, are then ordered according to the percentage of the genes in the pathway which were represented in the dataset. Table 2 shows the result after sorting.
Using our method, we identified the deregulation of mitochondrial pathways in the dataset which is in accordance with previous studies. The first cluster of genes involved was from the mitochondrial OXPHOS pathway. The OXPHOS pathway was well represented in the data with 93% of genes (106 out of 114) present in the dataset. Oxidative phosphorylation in mitochondria provides energy in the form of ATP generation. In muscle cells, mitochondrial dysfunction has been linked to insulin resistance and type-2 diabetes [8][9][10]. The involvement of genes coded by mitochondria in insulin resistance is also well known. The depletion of cellular mitochondrial DNA has been shown to cause insulin resistance in experimental model [11]. Reduced mitochondrial oxidative phosphorylation leads to the accumulation of intracellular triglycerides resulting in insulin resistance. The next 2 clusters, c20_U133 which is a manually curated cluster of genes coregulated with OXPHOS [3] and the mitochondrial gene cluster human_mitoDB_6_2002 reinforce that muscle mitochondrial dysfunction is linked to type-2 diabetes.

Conclusion
In this paper, we propose an innovative fuzzy-set-theorybased approach for differential analysis of gene pathways and apply it on identifying significant pathways for diabetes. Experiments have been conducted on both synthetic datasets and real world dataset. Results on real world diabetes data identified several number of gene pathways. Of note our top significant pathways were related to mitochondrial function which is well known to be involved in insulin resistance and type-2 diabetes. This approach can be used not only for pathway analysis of other diseases but also for other domains. As measuring the difference of two groups of data are essential to most of researches, our approach provides a solution to this general and most critical problem.

Methods
In [12][13][14], we proposed two fuzzy-set-theory based methods, CM-test and FM-test, to identify the individual genes that expressed significant differences under two conditions. In this paper, we extended the cluster misclassification concept to a multi-dimensional space and propose a new approach for pathway analysis, this approach, the expression values of a group of Q genes for a particular sample under a particular condition are considered as a Q-dimension vector. The differential analysis is done at the vector level, without individual gene differential statistic.
In this section, we first introduce the concept of fuzzy membership function of vectors, then the details of MCMtest.

Fuzzy membership Function of Vectors
In fuzzy set theory, the degree for one variable to belong to a fuzzy set is defined by a function. For a vector which has two dimensions, the degree that it belongs to a set of vectors can be defined by a three-dimensional function, with the third dimension being the measurement of the membership. Figure 5 shows a sample fuzzy membership function for a vector (x, y).
For vectors with n dimensions, their fuzzy membership function will be n+1-dimensional, with one dimension measuring the fuzzy membership.

Our approach
Consider a pathway that consists of Q genes, the problem now is to determine how these Q genes are expressed differently under two conditions. To answer this question, we consider the expression values of the Q genes for a particular sample under a particular condition as a Q-dimension vector. Then the expression values of a pathway under one condition j can be modeled as set S j (j = 1, 2) of points in a Q-dimension space. The idea is to consider the two sets of points S 1 and S 2 as samples from two different fuzzy sets. We then examine the membership value of each element with respect to these two fuzzy sets and determine the d-value between the two sets of samples.
The mean of the expression values of set S j is: where, N j is the number of samples in S j , is vector made by the expression values of the n-th sample under condition j.
We then characterize set S j (j = 1, 2) by a fuzzy set FS j (j = 1, 2) whose fuzzy membership function is defined as: where, Given an element in S 1 , we calculate its element misclassification degree with respect to FS 2 as We denote the misclassified elements in S 1 with respect to e e e f f where, and Then, the divergence between S 1 and S 2 can be calculated using the following: In our method, to negate the effect of outliers, we used αtrimmed mean instead of normal mean. The α-trimmed For computational simplicity, an Epanechnikov function shown as following can be used instead of the Gaussian function of equation (2): where,

One dimension: a special case
In this section we analyze MCM-test for it theoretical justification. For the sake of clarity, we start with one dimension, the simplest and special case of multi-dimension. The one dimensional MCM-test corresponds to differential analysis of a single gene.
The visualization tells us that they are different as they cover different areas and have different shapes. The CMtest, which can be considered as a special case of the MCM-test differentiate them by measuring the differences on the Y axis, which is a combined result of the location difference together with the difference of the variances.
MCM-test uses the probability distribution functions of these two distributions as their fuzzy membership functions respectively, and takes advantage of the membership differences of "misclassified" samples. As shown in Figure One dimension Gaussian distributions, μ1 = 600, μ2 = 700, σ1 = 50, σ2 = 100
A sample fuzzy membership function of vector (x, y)

Figure 5
A sample fuzzy membership function of vector (x, y).
6, a sample x 1 of D 2 has a higher degree of belonging to D 1 , thus is "misclassified" in MCM-test. This misclassification degree is aggregated with all the other "misclassified" samples of D 2 that are misclassified. Similarly, x 2 of D 1 has a higher degree for D 2 , thus is also misclassified. This misclassification degree is also aggregated with all the other misclassified samples of D 1 .
MCM-test collects all the misclassified degrees and the number of misclassified samples and form them into an index to measure the divergence of these two distributes. With the mean difference between these two distributions increases, the number of misclassified samples, as well as the aggregated misclassification degree decreases. Thus the MCM d-value will decrease. Figure 7(a) illustrates samples of two distributions, each of which is a 2-D Gaussian function. In pathway analysis, the X and Y axis can be the expression data of two individual genes respectively. Figure 7(b) shows the probability density functions of these two distributions, which can be used as their fuzzy membership functions after multiplying a constant.

Two and higher dimensions
Distributions of higher dimensions are hard to visualize. But the idea of the misclassification test stays the same. In multi-dimension space, each sample is a vector. And their misclassification degrees are used to measure the divergence of their distributions.   BioMedcentral BMC Bioinformatics 2008, 9(Suppl 6):S16 http://www.biomedcentral.com/1471-2105/9/S6/S16


extfieldsofstudy: Biology Medicine

text:
Quantitative Proteomics of a Presymptomatic A53T α-Synuclein Drosophila Model of Parkinson Disease*S

A global isotopic labeling strategy combined with multidimensional liquid chromatographies and tandem mass spectrometry was used for quantitative proteome analysis of a presymptomatic A53T α-synuclein Drosophila model of Parkinson disease (PD). Multiple internal standard proteins at different concentration ratios were spiked into samples from PD-like and control animals to assess quantification accuracy. Two biological replicates isotopically labeled in forward and reverse directions were analyzed. A total of 253 proteins were quantified with a minimum of two identified peptide sequences (for each protein); 180 (∼71%) proteins were detected in both forward and reverse labeling measurements. Twenty-four proteins were differentially expressed in A53T α-synuclein Drosophila; up-regulation of troponin T and down-regulation of fat body protein 1 were confirmed by Western blot analysis. Elevated expressions of heat shock protein 70 cognate 3 and ATP synthase are known to be directly involved in A53T α-synuclein-mediated toxicity and PD; three up-regulated proteins (muscle LIM protein at 60A, manganese-superoxide dismutase, and troponin T) and two down-regulated proteins (chaoptin and retinal degeneration A) have literature-supported associations with cellular malfunctions. That these variations were observed in presymptomatic animals may shed light on the etiology of PD. Protein interaction network analysis indicated that seven proteins belong to a single network, which may provide insight into molecular pathways underlying PD. Gene Ontology analysis indicated that the dysregulated proteins are primarily associated with membrane, endoplasmic reticulum, actin cytoskeleton, mitochondria, and ribosome. These associations support prior findings in studies of the A30P α-synuclein Drosophila model (Xun, Z. Y., Sowell, R. A., Kaufman, T. C., and Clemmer, D. E. (2007) Protein expression in a Drosophila model of Parkinson's disease. J. Proteome Res. 6, 348–357; Xun, Z. Y., Sowell, R. A., Kaufman, T. C., and Clemmer, D. E. (2007) Lifetime proteomic profiling of an A30P α-synuclein Drosophila model of Parkinson's disease. J. Proteome Res. 6, 3729–3738) that defects in cellular components such as actin cytoskeleton and mitochondria may contribute to the development of later symptoms.

Parkinson disease (PD) 1 is the second most common agerelated neurodegenerative disorder; it is characterized by loss of dopaminergic neurons in the substantia nigra coupled with formation of intracytoplasmic Lewy body (LB) inclusions (1,2). Both environmental perturbations and genetic abnormalities have been proposed to induce the development of PD (1). There is evidence that oxidative stress (3), mitochondrial dysfunction (4), and ubiquitin-proteasome system defects (5) are each associated with PD; however, the molecular pathways that induce dopamine neuron loss remain poorly understood, and early diagnostic tools and neuroprotective therapies are still lacking.
One promising approach to studying the underlying molecular mechanisms associated with PD involves the ␣-synuclein protein. This is primarily due to two factors: 1) ␣-Synuclein is linked to the disease as a primary component of LBs found in brains of PD patients (6), and 2) three missense mutations (A30P, A53T, and E46K) in the ␣-synuclein gene are linked to early onset familial PD (7)(8)(9). In addition, ␣-synuclein is linked to other neurodegenerative diseases. For example, it is a major constituent of insoluble inclusions found in dementias with LBs and multiple system atrophy (6). Additionally, an internal fragment of ␣-synuclein (residues 60 -95, termed the non-A␤ component) is found in amyloid plaques in the brains of patients with Alzheimer disease (10,11).
With these associations in mind, there has been a significant effort to develop ␣-synuclein animal models for PD (12); models are now available for a number of organisms, including yeast (13,14), flies (15), mice (16,17), rats (18,19), and primates (20). These models express (or overexpress) either wild-type or disease-linked mutant human ␣-synuclein. At present, no single model fully presents all human PD features; some fail to show dopamine neuron loss (17), whereas others do not develop true LBs (19). Thus, there is a need to investigate many different systems to understand underlying features that are general to disease mechanisms.
For the last decade, our group has been interested in the development of new high throughput analytical technologies for proteome analysis (21)(22)(23)(24). We have recently focused on Drosophila melanogaster (fruit flies). Drosophila has a relatively simple central nervous system and lacks an endogenous ␣-synuclein gene or ␣-synuclein gene ortholog (12); however, Feany and Bender (15) have shown that flies expressing both wild-type and mutated (A30P or A53T) human ␣-synuclein genes display symptoms that resemble those found in human PD patients. That is, the flies are viable but gradually exhibit progressive degeneration of dopaminergic neurons, formation of LB-like inclusions, and impairment of climbing ability. In addition, this organism has a relatively short life cycle, making it possible to carry out many studies with relative rapidity, and extensive studies at the level of the genome and proteome have been reported (25)(26)(27)(28)(29)(30)(31)(32)(33). Thus, the Drosophila model appears to be a useful tool for addressing issues associated with the underlying molecular mechanisms of ␣-synuclein-mediated neurotoxicity in PD.
In closely related work, we recently reported a proteome analysis of an A30P ␣-synuclein Drosophila model of PD at three different disease stages and a comparison of changes in gene expression profiles at the proteome and transcriptome levels (29). The studies indicated that as early as day 1 (presymptomatic stage) actin cytoskeletal and mitochondrial proteins were perturbed. In addition, our proteomics studies covering seven different time points across the adult life span implied that dysregulated proteins appear to be age-or disease stage-specific (30). This suggests that although analyses covering different disease stages help to elucidate disease progression, studies at the presymptomatic stage appear to be crucial for developing diagnostic tools, determining causation of disease, and intervening in neuron degeneration. Because Drosophila expressing A53T ␣-synuclein develop similar human PD-like symptoms as Drosophila expressing A30P ␣-synuclein (15), a proteomics study of the A53T mutant ␣-synuclein flies is expected to provide a more general (and possibly more comprehensive) view of protein changes that may be responsible for PD. Thus, in the present study we performed a quantitative proteomics analysis of an A53T ␣-synuclein Drosophila model of PD at the presymptomatic stage. This study also used a different experimental approach, an isotopic labeling strategy utilizing global internal standard technology (GIST) (23, 34 -36) in combination with multidimensional LC coupled to MS/MS for protein identification and quantification.

Drosophila Stocks and
Harvesting-This study utilized the following control and PD-generating fly genotypes: elav::Gal4 (Pw ϩmW.hs ϭ GawBelav C155 , Bloomington Stock Center, Indiana University) and UAS-A53T ␣-synuclein (the P(UAS-HsapßNCA.A53T)15.3 line was obtained from Mel Feany, Harvard Medical School), respectively. To obtain the elav::Gal4fUAS::A53T ␣-synuclein experimental flies (PDlike), virgin females from the elav::Gal4 line were crossed to males from the UAS-A53T ␣-synuclein stock. Control and PD-like flies were cultured on standard cornmeal medium, maintained at identical conditions (25 Ϯ 1°C), and harvested at the same time. To avoid differences that arise from gender, only male flies were used. A population of 250 adult fly heads was collected for each genotype at day 1 (within 24 h) posteclosion for protein extraction. Fly heads were collected and stored as described previously (29). A new batch of flies was raised for an independent biological replicate measurement.
Fly Head Sample Preparation-Fly samples were prepared as described elsewhere (29) with minor modifications. Briefly, fly head proteins were extracted using a mortar and electric pestle in a 0.2 M phosphate buffer saline solution (pH 7.0) containing 8.0 M urea and 0.1 mM phenylmethylsulfonyl fluoride. After centrifugation at 13,000 rpm for 10 min the supernatant was collected. A Bradford assay (Pierce) indicated that ϳ2.5 mg of proteins were obtained from 250 adult fly heads. Three standard proteins (i.e. human hemoglobin (purity unavailable), human albumin (96 -99%), and horse heart myoglobin (Ն90%) (Sigma-Aldrich)) were each spiked into equal amounts of control and PD-like fly samples at known concentration ratios of 2:1, 1:1, and 1:2, respectively. Protein mixtures were reduced, alkylated, and tryptically digested as described previously (29). Finally, tryptic peptides were cleaned, dried, and stored at Ϫ80°C until further analysis.
Differential Isotopic Labeling of Tryptic Peptides-An equal amount of control and PD-like fly tryptic peptides was resuspended in phosphate buffer (pH 7.5) to a 1 mg⅐ml Ϫ1 solution. The isotopic labeling reactions were processed as described by Regnier and co-worker (34). Briefly, a 100-fold molar excess of N-acetoxy-d 0 -succinimide and N-acetoxy-d 3 -succinimide was added to control or PD-like samples (In one experiment, the control sample was light labeled, and the PD-like sample was heavy labeled; we refer to this as forward labeling. In a second experiment with an independent biological replicate, the control sample was heavy labeled, and the PD-like sample was light labeled; we refer to this as reverse labeling.). The reactions proceeded for 5 h with constant stirring at room temperature. At the end of the reaction, two equal aliquots of samples were combined and treated with an excess amount of N-hydroxylamine at pH 10 -11 for 20 min. Finally, the isotopically labeled peptide mixture was adjusted to pH 7, cleaned, dried, and stored at Ϫ80°C until further analysis.
Strong Cation Exchange (SCX) Chromatography-Differentially labeled tryptic peptides were dissolved in 5.0 mM potassium phosphate buffer in 75:25 water:acetonitrile at pH 3.0. The peptide solution was injected onto a javelin guard column (10 ϫ 2.1 mm) that preceded a polysulfoethyl aspartamide column (100 ϫ 2.1 mm, 5 m, 200 Å; PolyLC Inc., Southboro, MA). The gradient was delivered at a flow rate of 0.2 ml⅐min Ϫ1 by a Waters 600 multisolvent delivery system, and peptides were detected at 214 nm by a Waters 2487 dual absorbance detector. Mobile phases consisted of 5 mM potassium phosphate in 75:25 water:acetonitrile at pH 3.0 (solvent A) and solvent A with the addition of 350 mM potassium chloride (designated as solvent B). Binary gradients with respect to the percentage of solvent B were as follows: 0 -5 min, 0%; 5-45 min, 0 -40%; 45-90 min, 40 -80%; 90 -100 min, 80 -100%; 100 -110 min, 100%; 110 -111 min, 100 -0%; and 111-121 min, 0%. One-minute collections into 96-well plates (Corning Inc., Corning, NY) over the 121-min gradient were combined into six fractions as follows Eluted peptides were analyzed using a hybrid LTQ-FT mass spectrometer. The instrument was operated to acquire a full FT-MS scan (m/z range of 300 -2000) followed by MS/MS scans of the top three most intense ions in the LTQ with data-dependent function enabled. The resolution was set to 100,000 (at m/z 400) for the survey FT-MS scan. The data-dependent acquisition uses a 30-s exclusion duration time and a collision energy of 35%.
Data Analysis-Raw MS/MS spectra (.RAW) were processed using the Xcalibur software package Bioworks (version 3.3.1, Thermo-Electron) to create a peak list (.DTA) with 50-ppm precursor ion tolerance. Individual .DTA files were grouped according to charge states of precursor ions using an in-house written algorithm. Chargesorted .DTA files were submitted to MASCOT (Matrix Science, Version 2.1) and searched against the National Center for Biotechnology Information nonredundant (NCBInr) Drosophila protein database (28,642 sequence entries) that is correlated with FBgn accession numbers in the FlyBase database for Drosophila protein identification. The same files were also searched against a home-built database containing protein sequences of human albumin, hemoglobin, and horse heart myoglobin for standard protein assignment. Carbamidomethylation of cysteine residues was used as a fixed modification. Variable modifications included acetylation (light or heavy) of lysine residues and the N termini of peptides. Additional parameters included a maximum of two trypsin miscleavages, a precursor tolerance of 15 ppm, and an MS/MS tolerance of 0.8 Da. Spectra that led to scores at or above the MASCOT-assigned homology score (the homology score defines a spectral match at a 95% confidence level) were assigned to specific peptide sequences; peptide sequences matching to multiple FBgn accession numbers were discarded (i.e. only peptides having sequences that were unique to a single protein were considered). To estimate the false positive rate of peptide identifications, we searched one of the most peptide-rich fractions (SCX fraction 3) against the reverse NCBInr Drosophila database (28,642 sequence entries) and the FlyBase database using the same parameters described above; the false positive rate calculated according to the formula by Gygi and co-workers (37) was 2.2% for doubly charged peptides and 3.4% for triply charged peptides.
Abundance ratios of differentially labeled peptides were computed using peak intensities from extracted ion chromatograms of light or heavy labeled peptides with an in-house written algorithm as described previously by our laboratory (38). For peptides identified in multiple SCX fractions or charge states, the intensities of peaks were summed to obtain abundance ratios. Relative quantification of proteins was obtained by averaging the intensity ratios of multiple derived peptides. Only proteins with a minimum of two peptide sequence identifications from the two independent experiments were considered for quantification (this requirement is expected to substantially lower the false positive rate associated with protein identification using the MASCOT algorithm). Abundance ratios for proteins reported as differentially expressed in this study were confirmed by manual inspection. The criteria for passing the manual inspection were as follows: (i) signal-to-noise ratios of both light and heavy labeled peptide pairs Ն10, (ii) extracted ion chromatogram must show approximate Gaussian shape as well as no coeluting ions, and (iii) light and heavy labeled peptide ion spectra must show approximate theoretically predicted isotope patterns and expected mass shift between doublet clusters.
Western Blot Analysis-For Western blot analysis, a new batch of male control and A53T ␣-synuclein flies was raised and harvested to obtain a total of 250 adult fly heads each (an independent batch of flies was utilized for Western blot analysis of individual proteins in this study, i.e. troponin T and fat body protein 1). Proteins were extracted (as described above) in 400 l of ice-cold 50 mM Tris buffer (pH 7.5) containing 10 mM NaF, 1 mM Na 3 VO 4 , 1 mM phenylmethylsulfonyl fluoride, 50 mM dithiothreitol, and 4% CHAPS. Protein concentrations were determined with a Bradford assay (Pierce). Equal amounts of proteins were separated by SDS-PAGE with 8% polyacrylamide gels and electroblotted onto polyvinylidene difluoride membranes. Membranes were blocked overnight at 4°C in Odyssey blocking buffer (LI-COR, Lincoln, NE) and simultaneously incubated with anti-␤-tubulin antibody (1:2500 dilution; Developmental Studies Hybridoma Bank, Iowa City, IA) and anti-troponin T antibody (1:500 dilution; Babraham Bioscience Technologies, Cambridge, UK) or anti-fat body protein 1 polyclonal antibody (1:500 dilution; kindly provided by Qisheng Song) for 2 h at room temperature with gentle shaking. Next, infrared dye 800-conjugated affinity-purified goat anti-rat IgG (Rockland Inc., Gilbertsville, PA) and infrared dye 700-labeled goat antimouse IgG (LI-COR) or anti-rabbit IgG (LI-COR) secondary antibodies were added and allowed to incubate for 2 h at room temperature. Immunoblotting bands were detected and quantified utilizing the LI-COR Odyssey infrared imaging system (simultaneous two-color targeted analysis) and software (LI-COR).

Quantification of Internal Standard Proteins-
The present study used the GIST coupled to LC-MS/MS approach for protein identification and quantification (Fig. 1). Standard proteins were spiked at an early stage of sample preparation and at known concentration ratios into equal amounts of control and PD-like fly proteins to assess the variability that arises from sample handling and preparation. Human hemoglobin, human albumin, and horse heart myoglobin were chosen as internal standards because BLASTP searches indicate that there are no identical or homologous tryptic peptide sequences in the Drosophila NCBInr genome database. The spiked ratios for hemoglobin, albumin, and myoglobin were 2:1 (down-regulated), 1:1 (not regulated), and 1:2 (up-regulated), respectively, in PD-like samples compared with controls. In the forward labeling measurement, a total of 10, three, and seven peptides were quantified for hemoglobin, albumin, and myoglobin, respectively. The observed average ratio was 0.45 Ϯ 0.07, 0.99 Ϯ 0.16, and 2.35 Ϯ 0.35, respectively, as given in Table I. In the reverse labeling measurement, nine, three, and six peptides were detected for hemoglobin, albumin, and myoglobin with an observed average ratio of 0.45 Ϯ 0.09, 1.31 Ϯ 0.14, and 2.69 Ϯ 0.47, respectively (Table I). Based on these results from internal standard proteins, we expect that this experimental approach accurately yields relative protein amounts in PD-like and control fly samples.
Quantitative Proteomics of a Drosophila Model of PD-To increase the confidence of protein quantification, we used forward and reverse labeling with two independent biological sets of animals. Fig. 2 shows an example of mass spectra of light and heavy labeled peptide pairs from the forward and reverse labeling measurements of the protein chaoptin (encoded by chaoptic). From the relative intensities of the exam-ple peptide observed as [LAVLDLSHNR ϩ 2H] 2ϩ ion, it is apparent that the peptide shows down-regulation in PD-like flies in both the forward and reverse labeling measurements (Fig. 2, top). A total of nine peptides were identified and quantified for chaoptin, comprising ϳ11% sequence coverage. Seven of the nine peptides were in common between the forward and reverse labeling experiments, and all nine exhibited down-regulation in PD-like flies in comparison with controls (Fig. 2, bottom), indicating down-regulation of the protein.
Although a single peptide can be utilized for the quantification of proteins (39), in the present study we required a minimum of two (arbitrarily chosen) peptides for a protein to be considered for quantification from the forward and reverse labeling experiments. In total, 253 proteins (supplemental Table SI) meet this criterion, among which 180 proteins (71%) were quantified in both forward and reverse labeling measurements (Fig. 3). A total of 61 and 12 proteins were uniquely quantified with multiple peptides from the forward and reverse labeling experiments, respectively. For proteins quantified in both forward and reverse labeling experiments, similar -fold-changes were obtained for the majority of the proteins (i.e. 157 of 180 (87%) proteins had relative S.D. values Յ25% between the two experiments). A  linear regression analysis of the relative abundance ratios commonly obtained from the two experiments gave a Pearson product moment correlation coefficient of 0.69, and the p value from a Student's t test (two-tailed distribution and two-sample unequal variance) was 0.84 (p Ͼ Ͼ 0.05). These analyses indicate that the two independent experiments were positively correlated and did not differ significantly. However, there were five proteins that differed by a relative S.D. Ն 50% between the two measurements. Ribosomal protein S3A (FBgn0017545) exhibited fold changes in opposite directions. We speculate that this inconsistency typ-ically arises from the use of different peptides that are identified in the separate experiments that may have varying ionization efficiencies. For example, the ribosomal protein S3A-derived peptide ion [LLELHGDGGGK ϩ 2H] 2ϩ identified only in the forward labeling experiment showed a 0.59fold change, whereas the peptide ion [IYPLHDVYIR ϩ 2H] 2ϩ identified only in the reverse labeling experiment exhibited a 1.98-fold change. Similar discrepancies in peptide foldchange values have also been observed using an isotopecoded affinity tag approach (40). As discussed previously by others, poor agreement in relative abundance of different peptides from the same protein may also arise due to posttranslational modifications (39).
Similar to many other quantitative proteomics analyses (39 -42), the majority of proteins identified in the present study did not change in PD-like flies compared with controls (Fig. 3). Currently, various criteria have been utilized to determine differential expression of proteins based on data quality (39 -43). For instance, Griffin et al. (43) applied a 1.5-fold difference criterion from the ICAT method as a threshold indicating significant change, and Chiang et al. (40) considered a protein with a calibrated ratio (ICAT analyses calibrated by Western blot analysis) of either Ն1.23 or Յ0.77 to be differentially expressed. The present study utilized three proteins as internal standards at different concentration ratios at an early stage of sample preparation to estimate experimental variability, e.g. isotopic labeling reagents and sample handling variability. On the basis of the quantitative results of internal standard proteins (discrepancies of Ϫ10.6 to 34.4% between the measured and expected values), we considered a 1.5-fold expression difference as a suitable threshold for significant changes (We note that individual peptides from the same protein can differ drastically, causing decent standard deviation among the measured peptide ratios. A protein is considered differentially expressed if it exhibits Ն1.5-fold change from multiple peptides in both forward and reverse labeling experiments despite large standard deviations among different peptides.). Proteins that exhibited Ն1.5-fold changes in only one of the experiments were discarded (these include proteins that did not show change or were not identified in the other experiment) to eliminate suspicious protein candidates. A total of 24 proteins were differentially expressed; they are listed in Table II. Among the dysregulated proteins, nine proteins were down-regulated that are encoded by fat body protein 1, chaoptic, retinin, odorant-binding protein 99b, retinal degeneration A, karst, Rtnl1, calnexin 99A, and cytochrome P450 reductase; the remaining 15 proteins that were up-regulated in PD-like flies are encoded by paramyosin, upheld, ribosomal protein L6, glycerol-3-phosphate dehydrogenase, muscle LIM protein at 60A, ATP synthase-␤, superoxide dismutase 2 (manganese), failed axon connections, ribosomal protein L14, ribosomal protein S8, heat shock protein cognate 3, ribosomal protein L23A, and three unnamed genes, i.e. CG32029, CG7675, and CG15006. The proteins encoded by chaoptic, upheld, paramyosin, and ATP synthase ␤ subunit were quantified with a minimum of six peptides from each independent measurement (a minimum of nine peptides from two biological experiments). An increased number of peptides that are used for quantification should increase the reliability of determining protein abundance changes.

Validation of Protein Expression by Western Blot Analysis-
To validate protein changes observed in the present study, we considered Western blot analysis. For instance, Drosophila troponin T (encoded by upheld) was identified with a total of nine peptides from the GIST-coupled LC-MS/MS approach. Example mass spectra for [DAGLGLSSAAMER ϩ 2H] 2ϩ and [LETEKYDLEER ϩ 2H] 2ϩ peptides belonging to troponin T show up-regulation in PD-like flies based on the relative intensities of peaks (Fig. 4, a and b). Seven other peptides identified also display up-regulation in PD-like flies compared with controls (Fig. 4c), suggesting up-regulation of the protein (i.e. 1.82 Ϯ 0.22 from the forward labeling experiment and 1.63 Ϯ 0.21 from the reverse labeling measurement). Consistent with these changes, Western blot analysis also showed up-regulation of the protein (Fig. 4d, top). In the present study, expression of ␤-tubulin (multiple ␤-tubulin isoforms) did not change in PD-like flies in comparison with controls (i.e. 0.97 Ϯ 0.28 from the forward labeling experiment and 0.96 Ϯ 0.07 from the reverse labeling experiment) and thus was utilized as a loading control (Fig. 4d, bottom). With normalization of the integrated intensities of the immunoblot-ting bands of troponin T to ␤-tubulin, a 1.51-fold up-regulation of troponin T was obtained in PD-like flies with a 13.2 and 5.4% relative S.D. from the forward and reverse labeling measurements, respectively. Another example is fat body protein 1, which exhibits down-regulation in PD-like flies from the proteomics approach (Table II). Western blot analysis detected three main bands (Fig. 5) that correspond to the FBP-1 protein (118 kDa) and two FBP-1 fragments (69 and 50 kDa); this is consistent with previous reports (44,45). Western blot analysis indicated that the native FBP-1 protein levels do not exhibit substantial differences between PD-like flies and controls. However, the two FBP-1 fragments were at lower levels; there was a 0.64-and 0.37-fold down-regulation of these fragments at 69 and 50 kDa, respectively. Overall, there was a 0.43-fold down-regulation for the sum of the three detected bands in PD-like flies in accordance with our proteomics results (i.e. an average of 0.37-fold down-regulation from the forward and reverse labeling experiments). Thus, these Western blot analyses provide evidence, in addition to the internal standards, that the quantitative results obtained with the GIST approach are sufficiently accurate. FIG. 4. Data from peptides belonging to troponin T. a, example mass spectra for light and heavy labeled peptide (DAGVLGLSSAAMER) pairs from the forward labeling measurement. b, example mass spectra for light and heavy labeled peptide (LETEKYDLEER) pairs from the reverse labeling measurement. c, column representation of identification and quantification of all nine identified peptides. d, Western blot analysis of troponin T with ␤-tubulin as the loading control.

DISCUSSION
Overview-The results presented above describe a quantitative proteome analysis of an A53T ␣-synuclein Drosophila model of PD utilizing forward and reverse isotopic labeling coupled with an LC-MS/MS approach. Two independent biological replicates (PD model and control animals) were used. Quantification was aided by additional incorporation of internal standards, i.e. tryptic peptides from proteins at defined concentration ratios. These internal standards allowed us to assess the experimental variability associated with isotopic labeling reagents, such as preferential labeling, inefficient labeling, and variation in ionization efficiency, sample handling, and preparation. The percent errors obtained in our study ranged from Ϫ10.6 to 34.4%. This provides confidence that a 1.5-fold difference in abundance of light and heavy labeled peptide pairs is a suitable criterion to determine that a protein is significantly differentially expressed.
A total of 253 proteins were quantified with a minimum of two peptides. Of these, 24 proteins (Table II) met our criteria for differential expression in the PD-like flies relative to the controls (We note that the relatively high percentage (i.e. 10%) of dysregulated proteins is closely associated with a relatively small number of proteins (i.e. 253) that were considered for quantification (described in detail below), and this percentage may not be applicable to the remaining of the fly proteome. Thus, we caution that it may be inappropriate to deduce that a global disruption of the proteome was induced at the presymptomatic stage in the PD-like animals from these data.). Up-regulation of troponin T and down-regulation of fat body protein 1 were confirmed by Western blot analysis, providing confidence of our experimental design. Collectively, we expect that the present quantitative proteomics approach provides valuable information about protein alter-ations that appear to be associated with PD at the presymptomatic stage.
The relatively low proteome coverage found in the present study primarily arises from several factors. First, high abundance proteins (e.g. myosin and actin) were not removed during sample preparation. These proteins may mask detection of many low abundance proteins, e.g. myosin heavy chain contributes to ϳ6% of the total identified peptides (see supplemental Table SI). Second, the isotopic labeling approach used in the study acetylates the primary amines (N termini and lysine residues), removing positive charge and thus making the lysine-containing peptides harder to detect. Third, most of the proteins (i.e. 572) identified in our study were assigned with a single peptide; however, we required a minimum of two peptides for quantification to be more confident in changes. Other factors that may be associated with low proteome coverage include database searching parameters used (e.g. a stringent precursor mass tolerance (15 ppm) was utilized, and variable protein post-translational modifications were not considered when running MASCOT searches); thus, this approach may miss some protein identifications. Overall, the 253 proteins assigned correspond to abundant proteins in flies based on our data-dependent acquisition method (i.e. generally, the number of peptides detected positively correlate with the protein). The cellular compartments associated with the 253 proteins can be obtained from Gene Ontology analysis (46); however, these are not described in this study. Rather we focused our attention on the differentially expressed proteins, and the cellular compartments of the 24 proteins are presented in Fig. 6, which shows that five have no available cellular component annotation, four are ribosomal proteins, four are membrane proteins, four are from the endoplasmic reticulum (ER), three are actin cytoskeletal proteins, two are mitochondria-associated proteins, and the last two are assigned to the extracellular and cytoplasmic compartments. Below we provide a brief discussion of the relevance of individual proteins.
Membrane Proteins-We identified four membrane-associated proteins whose expression was perturbed in A53T ␣-synuclein-expressing flies in comparison with controls. Chaoptin, a glycoprotein that localizes to the extracellular surface of the plasma membrane by covalent interaction with glycosylphosphatidylinositol, plays a key role in photoreceptor cell morphogenesis by serving as a photoreceptor-specific cell adhesion molecule (47)(48)(49). Drosophila chaoptin mutants display remarkable membrane abnormalities (49). The protein encoded by retinal degeneration A (rdgA) is associated with the transport of phospholipids to the photoreceptive membranes, and the Drosophila rdgA mutant induces degeneration in photoreceptor cells within a week posteclosion (50). Down-regulation of both chaoptic and rdgA at the protein level suggests disruption of the structural integrity of neuronal cell membranes that could lead to failure in cell communications and subsequent malfunction of cellular processes. This hypothesis appears to be supported by a previous report that suggests that ␣-synuclein plays a role in membrane transport and synaptic membrane biogenesis, and A53T mutant ␣-synuclein likely disrupts the plasma lipid bilayers (51). Because the plasma membrane serves as the attachment point for intracellular cytoskeletal structure, dysregulation of membrane-associated proteins could be linked to cytoskeletal defects.
Cytoskeletal Proteins-We observed three actin cytoskeleton-associated proteins that were disturbed. Troponin T, encoded by upheld, is a member of the actin filament-associated troponin complex (52). Drosophila troponin T has sequences similar to vertebrate troponin T (53), and the importance of troponin T has been demonstrated in both vertebrates and invertebrates (52)(53)(54). For example, mutations in cardiac troponin T causes familial hypertrophic cardiomyopathy in humans (54); similarly, mutations in Drosophila troponin T induce muscle abnormalities (53). Muscle LIM protein at 60A (Mlp60A) was up-regulated in A53T ␣-synuclein-expressing flies relative to controls (Table II). Mlp60A is a member of the cysteine-rich protein family that plays pivotal roles in cell differentiation (55). Mlp60A is associated with the actin cytoskeleton and plays a role in myogenic differentiation (56,57). Arber et al. (57) found that muscle LIM protein was strongly up-regulated in denervated (or paralyzed skeletal muscle) in adult rats; substantially high levels of muscle LIM protein appear to be toxic or to interfere with cell differentiation. Thus, up-regulation of Mlp60A at the presymptomatic stage in PD-like flies appears to corroborate the notion that expression of A53T ␣-synuclein may be deleterious to actin cytoskeletal muscles. In addition to up-regulation of troponin T and Mlp60A, the thick filament structure protein paramyosin was also up-regulated in A53T ␣-synuclein flies. The observation of functional perturbations in the actin cytoskeleton agrees with our previous finding in an A30P ␣-synuclein Drosophila model of PD (29,30). Collectively, our studies suggest that actin cytoskeletal defects may play a role in the onset of PD-like symptoms in flies.
ER Proteins-Of the four proteins associated with ER, the change in heat shock protein cognate 3 (Hsc3p) is especially intriguing. Hsc3p is a member of the Hsp70 family, a class of molecular chaperones that are associated with a diversity of important cellular activities, such as protein folding and protein degradation (58). Increased levels of Hsc3p alleviate fly lethality caused by D231S mutant Hsc3p (59). Overexpression of Hsp70 has been found to rescue ␣-synuclein-medicated toxicity in Drosophila (60). We hypothesize that increased Hsc3p levels in A53T ␣-synuclein-expressing flies may be an indication that cells are encountering an unfavorable environment (e.g. increased concentrations of misfolded proteins). Because Hsc3p is the only ER member in the Hsp70 family (59), overexpression of the ER Hsp70 as a result of expression of A53T ␣-synuclein may induce ER stress (in contrast, HSC4 protein, the most abundant cytoplasmic Hsp70 member, did not exhibit change in A53T ␣-synuclein flies, i.e. 1.07 Ϯ 0.07 from the forward labeling experiment and 1.14 Ϯ 0.19 from the reverse labeling experiment). It is worth noting that increased ER stress was also observed in rat PC12 lines as a consequence of induction of A53T ␣-synuclein (61). The remaining three ER proteins (encoded by Rtnl1, calnexin 99A, and cytochrome P450 reductase) are involved in proton and intracellular protein transport (62) and are down-regulated in PD-like flies.
Mitochondrial Proteins-Two key mitochondrial proteins, manganese-superoxide dismutase (Mn-SOD) and ATP synthase ␤ subunit (␤-ATPase), were observed at higher levels in A53T ␣-synuclein flies. The Mn-SOD enzyme is an antioxidant that catalyzes the conversion of reactive oxygen species generated by mitochondrial superoxide to hydrogen peroxide (63) and thus is implicated in cellular defense (preventing oxidative damage). Overexpression of Mn-SOD attenuates 1-methyl-4phenyl-1,2,3,6-tetrahydropyridine-mediated toxicity (64) and protects against adriamycin-induced cardiomyopathy in transgenic mice (65). Yeast Flp recombinase-mediated overexpression of Mn-SOD extends life span in Drosophila (63). Increased levels of Mn-SOD in A53T ␣-synuclein flies suggest that mitochondria may encounter increased oxidative stress. Additionally, mitochondrial disruption can also be seen from the up-regulation of the nuclearly encoded mitochondrial ␤-ATPase, which plays an essential role in the function of the enzyme ATP synthase (66). This protein was also up-regulated in A30P ␣-synuclein flies at early disease stage (29), and ATP synthase D was up-regulated in human substantia nigra of PD patients compared with controls (67). From these studies, it seems reasonable to hypothesize that alterations in ATP synthase may be responsible for the malfunction of mitochon- The Drosophila protein interaction database utilized contains protein interaction data generated using the yeast two-hybrid system (YTH) at Curagen (gray line), the Finley laboratory (blue line), and Hybrigenics (cyan line). Differentially expressed proteins are color-coded according to their subcellular localizations. Proteins (only direct target-interacting proteins are shown) interacting with the differentially expressed proteins are uniformly represented by a gray circle irrespective of their subcellular localizations. This map consists of seven distinct networks: one large network contains 96 proteins, one has 26 proteins, one has 16 proteins, one has eight proteins, one has six proteins, one has five proteins, and seven have two proteins. TBPH, TAR DNA-binding protein homolog; see Table II for other gene symbols.
dria that is widely accepted as a pathological feature of human PD (5).
Ribosomal Proteins-Four ribosomal proteins were up-regulated in A53T ␣-synuclein flies: ribosomal proteins L6, L14, L23A, and S8. These ribosomal proteins are structural components of the ribosome, the cellular machinery that synthesizes proteins. Abundance changes in structural constituents of the ribosome suggest that cellular metabolism may be disturbed due to the expression of A53T ␣-synuclein. Elevated expressions of the ribosomal proteins in the A53T ␣-synuclein flies may be associated with perturbed abundances of other proteins.
To test the hypothesis that the dysregulated proteins may be connected by intermolecular interactions, we performed a Drosophila protein interaction network analysis using a Webbased tool, Interaction Map Browser, developed by Finley and co-workers (68). By searching the 24 dysregulated proteins against a comprehensive Drosophila protein interaction database combined from the Finley laboratory (69), Curagen (70), and Hybrigenics (71), we found that five proteins have no available protein interactions, seven proteins have one single interaction, and the remaining 12 proteins have multiple interactions (Fig. 7). The top three most abundant interacting proteins are ribosomal protein S8 (35 interactions), paramyosin (25 interactions), and ATP synthase ␤ subunit (22 interactions). A detailed view of the protein interaction network analysis map is shown in Fig. 8. It is interesting to mention that of the 12 proteins that have multiple interactions seven form a single interaction network bridged by ribosomal protein S8 (Fig. 8), including six up-regulated proteins (glycerol-3-phosphate dehydrogenase, ␤-ATPase, ribosomal protein L6, ribosomal protein S8, Hsc3p, and ribosomal protein L14) and one down-regulated protein encoded by Rtnl1. These potential protein associations may provide insights into molecular pathways underlying PD.
Finally because transgenic Drosophila expressing human A53T ␣-synuclein genes develop human PD-like symptoms after day 10 (15), dysregulation of proteins observed at day 1, especially those that were known to be A53T ␣-synuclein toxicity-or PD-associated, may shed light on potential diagnostic strategies at the presymptomatic stage. Comprehensive studies of these proteins and their involved protein interactions (e.g. the single large protein network that contains seven of the 24 dysregulated proteins) and cellular pathways may provide insight into the molecular mechanisms underlying the causes of neurodegeneration in PD. Although several cellular malfunctions (e.g. mitochondrial dysfunction, oxidative stress, and impairment of the ubiquitin-proteasome system) have been proposed to be closely related to PD (5), the cause and effect relationships remain unknown. Altered proteins in the presymptomatic A53T ␣-synuclein Drosophila PD model were primarily associated with membrane, actin cytoskeleton, ER, mitochondria, and ribosome. These associations provide further evidence, in addition to our previous studies of the A30P ␣-synuclein Drosophila model, that defects in cellular components such as actin cytoskeleton and mitochondria may contribute to late onset of PD-like symptoms. Nevertheless, further studies are warranted to understand the relationships among the cellular defects that may induce dopaminergic neuron degeneration. Targeted studies involving dysregulated proteins identified at the presymptomatic stage may facilitate the elucidation of major cellular defects underlying PD.


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Differential Activation of ERK and Rac Mediates the Proliferative and Anti-proliferative Effects of Hyaluronan and CD44*

Hyaluronan, a widely distributed component of the extracellular matrix, exists in a high molecular weight (native) form and lower molecular weight form (HMW- and LMW-HA, respectively). These different forms of hyaluronan bind to CD44 but elicit distinct effects on cellular function. A striking example is the opposing effects of HMW- and LMW-HA on the proliferation of vascular smooth muscle cells; the binding of HMW-HA to CD44 inhibits cell cycle progression, whereas the binding of LMW-HA to CD44 stimulates cell cycle progression. We now report that cyclin D1 is the primary target of LMW-HA in human vascular smooth muscle cells, as it is for HMW-HA, and that the opposing cell cycle effects of these CD44 ligands result from differential regulation of signaling pathways to cyclin D1. HMW-HA binding to CD44 selectively inhibits the GTP loading of Rac and Rac-dependent signaling to the cyclin D1 gene, whereas LMW-HA binding to CD44 selectively stimulates ERK activation and ERK-dependent cyclin D1 gene expression. These data describe a novel mechanism of growth control in which a ligand-receptor system generates opposing effects on mitogenesis by differentially regulating signaling pathways to a common cell cycle target. They also emphasize how a seemingly subtle change in matrix composition can have a profound effect on cell proliferation.

The extracellular matrix (ECM) 2 is a key regulator of several cellular functions, including differentiation, survival, and pro-liferation. Hyaluronan (HA) is a widely distributed component of the ECM (1,2). Unlike ECM proteins such as collagen, fibronectin, vitronectin, and laminin, HA is a glycosaminoglycan with a molecular mass ranging from several million daltons (called native or high molecular weight HA (HMW-HA)) to 20 -500 kDa (called lower molecular weight HA (LMW-HA)). HMW-HA is produced and secreted from several cell types where it is incorporated into the ECM and interacts in an autocrine and paracrine fashion with its receptors. LMW-HA can be synthesized de novo or generated from the enzymatic degradation or oxidative hydrolysis of HMW-HA, typically at sites of inflammation (2,3). Very small HA oligomers also have biological activities (4,5), but their biological significance is less well understood.
HA can bind to several cell surface receptors (6,7), but the most extensively studied receptor for HA is CD44 (8,9). CD44 is a type I transmembrane glycoprotein with a 72-amino acid cytoplasmic domain (10). CD44 is an endocytic receptor for HA (11) and can also regulate classical signaling pathways, including Src family kinases and Rho family GTPases (12). Sequential activation of the metalloproteinase, MMP-1, and presenilin-dependent ␥-secretase releases a biologically active extracellular domain and intracellular domain from CD44, which appears to explain some of the effects of CD44 on gene transcription (13). CD44 plays an important role in regulation of the cortical actin cytoskeleton through an interaction with merlin and ERM proteins and also transmits signals that control differentiation, migration, and proliferation (14,15). In addition to HA, CD44 binds to other ligands, including osteopontin and MMPs (16,17). In fact, the binding of MMPs to CD44 promotes the MMPdependent activation of TGF-␤ (18). Thus, the signaling effects of CD44 can be direct, a consequence of cortical actin organization, or indirect through TGF-␤.
Both HMW-HA and LMW-HA bind to CD44 with similar affinities, but the biological outcome of the binding is distinct (10). For example, the binding of HMW-HA to CD44 antagonizes mitogen-induced cell cycle progression, whereas the binding of LMW-HA to CD44 stimulates cell cycle progression in vascular smooth muscle cells (SMCs) and fibroblasts (19). The molecular basis for these opposing proliferative effects of HMW-HA and LMW-HA remains unresolved. We recently reported that the anti-proliferative effect of HMW-HA reflects its ability to inhibit the mitogen-dependent induction of cyclin D1 mRNA and protein (20). This effect is mediated by CD44 and likely independent of TGF-␤ activation because the anti-proliferative effects of TGF-␤ are most closely linked to up-regulation of cyclin-dependent kinase (cdk) inhibitors, p15 INK4b and p21 cip1 (21). The signal transduction mechanism(s) by which HMW-HA binding to CD44 inhibits cyclin D1 gene expression remains unexplored.
The cyclin D1 gene is the target of many mitogenic signaling pathways, but the best studied pathway to cyclin D1 is arguably the ERK-MAPK pathway. In several cell types, a sustained ERK activity between 3 and 6 h after mitogen stimulation results in the induction of cyclin D1 mRNA in mid-G 1 phase, at least when cells are re-entering G 1 phase from quiescence (22,23). A second common way to induce cyclin D1 mRNA is through Rac (24,25). Rac signaling to cyclin D1 requires NF-B and results in a somewhat earlier induction of cyclin D1 within the G 1 phase (23,26). Once expressed, cyclin D1 binds to cdk4 or cdk6 and initiates the inactivating phosphorylation of Rb that, in turn, controls the transcription of E2F-dependent genes and progression into S phase (27,28).
In this study we show that the opposing effects of HMW-HA and LMW-HA on S phase entry do not reflect regulation of distinct cell cycle targets but rather opposing effects on the expression of cyclin D1 mRNA. Moreover, we demonstrate that HMW-HA inhibits cyclin D1 gene expression by selectively inhibiting Rac signaling to cyclin D1, whereas LMW-HA stimulates cyclin D1 gene expression by selectively stimulating ERK signaling to cyclin D1. Both effects require CD44. To our knowledge, this is the first study of a ligand-receptor system having opposing effects on mitogenesis by differentially regulating signaling pathways to a common cell cycle target.

EXPERIMENTAL PROCEDURES
Cell Culture-Early passage human vascular SMCs were maintained and serum-starved at near confluence for 48 h as described (20). Early passage vascular SMCs were isolated from wild-type and CD44-null C57Bl/6 mice and cultured as described (20). Primary mouse embryonic fibroblasts (MEFs) were isolated from E14.5 background-matched wild-type or Skp2-null embryos (29). The explanted MEFs were maintained in DMEM containing 10% FBS and used at passage 2-4. Near confluent MEFs were serum-starved in DMEM, 1 mg/ml heatinactivated fatty acid-free bovine serum albumin for 48 h. A concentrated solution of endotoxin-free LMW-HA solution (10 mg/ml; MP Biomedicals, Solon, OH) or HMW-HA (10 mg/ml; Healon, Advanced Medical Optics, Santa Ana, CA) was diluted to 200 g/ml with DMEM. Unless noted otherwise, the solutions were supplemented with FBS (to 10 or 0.1% final concentrations for experiments with HMW-HA or LMW-HA, respectively) and directly added to the serum-starved cells. S phase entry was determined by immunofluorescence microscopy as described (20,30) in cells incubated with BrdU from the time of FBS stimulation. Polymyxin B was typically added to the cells as described (20), although similar results were obtained in the absence or presence of polymyxin B. In some experiments, a species-specific blocking antibody to human CD44 (5F12; 50 g/ml), or an irrelevant isotype-matched antibody, was added to the cultures ϳ30 min before the addition of LMW-HA. The figures show representative results, typically from 3 to 4 independent experiments.
Adenoviral Infections and siRNA Transfections-Adenoviral infections were performed as described (20) using 10 6 human vascular SMCs in 100-mm dishes. The adenoviruses encoded LacZ, dominant negative Rac (RacN17), or a Rac-GAP (␤ 2 -chimerin). The infected, starved cells were washed once with serum-free DMEM before being incubated with 0.1% FBS in the presence or absence of LMW-HA (200 g/ml).
For siRNA-mediated knockdown, human vascular SMCs were trypsinized and reseeded (10 6 cells per 100-mm dish) in DMEM, 10% FBS without antibiotic overnight. The next day, Lipofectamine 2000 (Invitrogen; 1 l per 25,000 cells) and siRNAs (Ambion; 150 nM final concentration) were diluted in Opti-MEM (Invitrogen) and added to the confluent cells for 4 h before serum-starving the cells for an additional 24 h in fresh Opti-MEM (30). The cells were then incubated with 0.1% FBS in the presence or absence of LMW-HA for 24 h. The siRNA sequences used to knockdown cyclin D1 and Rac1 have been described (30). An additional Rac siRNA sequence used here was GAUUAUGACAGAUUACGCCtt. A nonspecific siRNA indicates where extraneous information was removed from the immunoblot. B, human SMCs were co-transfected with plasmids encoding the cyclin A promoter driving luciferase and CMV-driven Renilla luciferase as described (20,48). The transfected cells were serum-starved for 24 h and incubated with 0.1% FBS for 24 h. Cyclin A promoter activity was determined in the absence or presence of LMW-HA and plotted relative to Renilla luciferase activity. Quiescent human SMCs were also incubated with 0.1% FBS for 24 h in the absence or presence of LMW-HA. Total RNA was isolated and analyzed by QPCR for cyclin A mRNA and 18 S rRNA. C indicates control. C, quiescent human SMCs treated with LMW-HA were lysed, and the levels of cyclin D1 mRNA and 18 S rRNA were determined by QPCR. D, quiescent human SMCs treated with LMW-HA were lysed and immunoblotted for cyclin D1 and actin (loading control). E, quiescent human SMCs treated with LMW-HA were collected, lysed, and immunoblotted for p27, cdk2, and cdk4 (loading control). F, quiescent human SMCs treated with LMW-HA were lysed and analyzed by QPCR for p27 mRNA and 18 S rRNA. The expression of cyclin A, cyclin D1, and p27 mRNAs was normalized to 18 S rRNA.
Rac Activation Assay-Near-confluent SMCs (ϳ10 5 cells per 35-mm dish) were serum-starved for 48 h. The cells were preincubated with 200 g/ml HMW-HA or LMW-HA for 30 min at 37°C before being stimulated for 3 or 5 min with 10% FBS or 0.1% FBS, respectively. Rac activity was determined using a G-LISA Rac Activation Assay Biochem Kit (Cytoskeleton Inc., Denver, CO). Cells were collected and lysed as per the manufacturer's instructions. Error bars indicate S.D. of triplicate measurements.
Quantitative Real Time Reverse Transcriptase-PCR (QPCR)-Cells washed once with cold phosphatebuffered saline were extracted in TRIzol reagent (Invitrogen), and total RNA was isolated. The levels of cyclin D1 mRNA, Skp2 mRNA, cdk4 mRNA, and 18 S rRNA were determined by QPCR as described (20,30). QPCR results for cyclin D1 and Skp2 mRNAs were plotted relative to 18 S rRNA or cdk4 mRNA, neither of which varied reproducibly in response to the treatments used here. Error bars indicate S.D. of duplicate PCR reactions.
Immunoblotting-SDS-gel electrophoresis was performed under reducing conditions, and the fractionated proteins were transferred to nitrocellulose filters before immunoblotting with antibodies to cyclin D1, p27, cdk2, cdk4, Rac, Rb, and actin as described (23,31,32). The resolved proteins were detected using ECL (Amersham Biosciences). Autoradiograms were digitized by scanning, and figures were assembled using Adobe Photoshop.

LMW-HA Synergizes with a Suboptimal Mitogenic Stimulus
to Promote S Phase Entry-We previously reported that LMW-HA stimulates S phase entry in quiescent mouse vascular SMCs incubated in suboptimal (Ͻ1%) FBS, and that the effect requires CD44 (23). However, limitations in the numbers of murine vascular SMCs significantly hindered a detailed analysis of the underlying mechanism. We therefore confirmed the pro-mitogenic effect of LMW-HA, and the dependence on CD44, in human vascular SMCs (supplemental Fig. 1, A and B).
These initial experiments also showed that the potency of LMW-HA was similar to 10% FBS (supplemental Fig. 1A). Interestingly, LMW-HA could not stimulate S phase in the complete absence of FBS (supplemental Fig. 1C), indicating that LMW-HA binding to CD44 promotes mitogenesis by synergizing with a sub-optimal mitogenic stimulus. There was no apparent difference in cellular viability or morphology of vas-   NOVEMBER 14, 2008 • VOLUME 283 • NUMBER 46 cular SMCs incubated in the absence or presence of 0.1% FBS or LMW-HA (supplemental Fig. 1D).

Regulation of ERK, Rac, and Cyclin D1 by Hyaluronan and CD44
Cyclin D1 Is the Primary Target of LMW-HA-We then determined the effect of LMW-HA on G 1 phase cell cycle progression in human vascular SMCs. The inactivation of Rb is a mid-late G 1 phase event that is mediated by cdk4/6-and cdk2dependent phosphorylation, allows for the release of activator E2Fs, and is essential for S phase entry (28,33). LMW-HA stimulated Rb hyperphosphorylation as determined by gel shift (Fig.  1A); it also activated the cyclin A promoter, an E2F target (34,35), and induced cyclin A mRNA (Fig. 1B). We mapped the effect of LMW-HA within the G 1 phase cdks and found that LMW-HA stimulated the expression of cyclin D1 mRNA and protein (Fig. 1, C and D, respectively). LMW-HA also downregulated the protein levels of the cdk inhibitor, p27 (Fig. 1E), but did not affect the expression of p27 mRNA (Fig. 1F). Levels of the two major G 1 phase cdks, cdk4 and cdk2, were unaffected by LMW-HA (Fig. 1E). p21 levels were difficult to detect in these cells (data not shown). Collectively, these data indicate that the increased inactivation of Rb and S phase entry seen in response to LMW-HA results from up-regulation of cyclin D1, down-regulation of p27, and the consequent activation of constitutively expressed cdk4 and cdk2.
The levels of p27 are typically controlled by ubiquitin-mediated proteolysis through the ubiquitin-protein ligase complex, SCF Skp2 (36,37). Skp2 is the substrate specificity component of the SCF complex, and its mRNA and protein levels are induced by mitogens as cells progress toward S phase from quiescence (38). Consistent with these results, we found that LMW-HA stimulated the expression of Skp2 mRNA and protein ( Fig. 2A). A CD44-neutalizing antibody strongly inhibited the stimulatory effects of LMW-HA on cyclin D1 and Skp2 mRNAs (Fig. 2,  B and C, respectively). Furthermore, LMW-HA failed to stimulate the expression of cyclin D1 and Skp2 mRNAs in CD44-null SMCs (Fig. 2,  D and E). Thus, the effects of LMW-HA on both cyclin D1 and p27 require CD44.
We recently reported that the down-regulation of p27 commonly seen in response to mitogenic stimulation can be a secondary consequence of cyclin D1 induction and cdk4/6 activation; cdk4/6dependent inactivation of Rb promotes E2F release, which then induces Skp2 and decreases steadystate p27 levels (31,38). Indeed, we found that the effects of LMW-HA on Skp2 and p27 levels were secondary to the induction of cyclin D1 because cyclin D1 knockdown with siRNA ( Fig. 3A) eliminated the effect of LMW-HA on Skp2 gene induction (Fig. 3B) and p27 down-regulation (Fig. 3A). In contrast, knock-out of Skp2 did not prevent LMW-HA from inducing cyclin D1 mRNA (Fig. 3C), as would be expected if Skp2 up-regulation was a secondary (rather than primary) effect of LMW-HA. Collectively, these data indicate that cyclin D1 is the primary target of LMW-HA. Since our recent report showed that HMW-HA inhibits S phase entry by blocking the mitogen-dependent induction of cyclin D1 (20), we conclude that the opposing effect of HMW-HA and LMW-HA are both exerted through a common cell cycle target, cyclin D1. Consistent with this conclusion, knockdown of cyclin D1 strongly inhibited S phase entry in response to LMW-HA (Fig. 3D). This result also indicates that cyclin D1 is the major D-type cyclin regulating S phase entry in this system.

Opposing Effects of HMW-and LMW-HA on ERK-and Racdependent Cyclin D1 Gene Expression-The ERK-MAPK and
Rac pathways have frequently been linked to the induction of cyclin D1 mRNA (see Introduction). We examined the effect of these pathways on FBS-stimulated cyclin D1 gene expression in human vascular SMCs and asked if these pathways might be differentially regulated by HMW-HA and LMW-HA. We found that FBS-stimulated induction of cyclin D1 mRNA (Fig.  4A) and protein (Fig. 4D) were not strongly affected by blocking ERK activation with the MEK inhibitor U0126. In contrast, ectopic expression of dominant negative Rac (RacN17), a Rac-

. Cyclin D1 gene expression is regulated by Rac in serum-stimulated human vascular SMCs.
A, quiescent human vascular SMCs were stimulated with 10% FBS for the selected times in the presence of DMSO or 50 M U0126. Total RNA was then extracted and used to determine levels of cyclin D1 mRNA and 18 S rRNA by QPCR. Inset, cells were collected, lysed, and immunoblotted for dually phosphorylated ERK1/2 (pERK1/2) and ERK2 (loading control). B, human vascular SMCs were infected with an adenovirus (100 multiplicity of infection) encoding LacZ, dominant negative Rac (N17Rac), or a Rac-GAP (␤2-chimerin), serumstarved, and stimulated with 10% FBS for the selected times. Total RNA was extracted and used to determine levels of cyclin D1 mRNA and 18 S rRNA by QPCR. C, quiescent human vascular SMCs were transfected with a nonspecific siRNA (control; C) or two distinct Rac1 siRNAs (also called R-1 and R-2) before being stimulated with 10% FBS for the selected times. Total RNA was extracted and used to determine levels of cyclin D1 mRNA and 18 S rRNA by QPCR. Inset, cells were collected, lysed, and immunoblotted for Rac and GAPDH (loading control). The expression of cyclin D1 mRNA was normalized to 18 S rRNA. D, experiments in A and C were repeated, and collected cells were analyzed by immunoblotting for the levels of cyclin D1 and GAPDH (loading control).
We then asked if HMW-HA and LMW-HA differentially affected ERK or Rac signaling to cyclin D1 mRNA. We examined sustained ERK activity (3-9 h after mitogenic stimulation) because that signaling period has been linked to the induction of cyclin D1 mRNA (see Introduction). We focused on much earlier times for Rac activation because the mitogen-stimulated GTP loading of Rac is known to decay rapidly. We found that the inhibitory effect of HMW-HA on FBS-stimulated cyclin D1 gene expression (Fig. 5A) was not associated with a decrease in ERK activation (Fig. 5B). However, HMW-HA strongly inhibited GTP loading of Rac in human vascular SMCs (Fig. 5C). Conversely, the stimulatory effect of LMW-HA on cyclin D1 gene expression (refer to Fig. 1) was not associated with a stimulation of Rac-GTP loading (Fig. 5C) nor did Rac siRNA inhibit the induction of cyclin D1 mRNA or S phase entry in LMW-HA-treated cells (Fig. 6, A-C). LMW-HA did result in the efficient activation of ERKs (assessed by immunoblotting for dually phosphorylated ERK1/2) in vascular SMCs incubated with 0.1% FBS (Fig. 6D). Importantly, the stimulatory effects of LMW-HA on ERK activity and cyclin D1 induction are causally linked because MEK/ERK inhibition with U0126 blocked the induction of cyclin D1 mRNA in response to LMW-HA (Fig. 6E). Collectively, these data show that HMW-and LMW-HA exert their opposing effects on cyclin D1 gene expression by differentially regulating the activation of Rac and ERK. By extension, we conclude that HA binding to CD44 can have opposing effects on mitogenesis because the high and lower molecular weight forms of HA differentially regulate the ERK-and Racsignaling pathways to cyclin D1 mRNA.

DISCUSSION
The results described here document the mechanism underlying the pro-mitogenic effect of LMW-HA, and begin to explain how HMW-HA and LMW-HA can have opposing effects on cell cycle progression despite binding to the same receptor, CD44. We show that LMW-HA stimulates the induction of cyclin D1 in cells exposed to a suboptimal mitogenic stimulus. LMW-HA also stimulates the expression of Skp2 and the down-regulation of p27 kip1 , but epistasis experiments (in which we depleted either cyclin D1 or Skp2) showed that the effects on Skp2 and p27 likely result from cyclin D1-dependent Rb inactivation. Thus, the effect of LMW-HA on cyclin D1 levels is primary and its effects on Skp2 and p27 levels are secondary.
It is noteworthy that HMW-HA exerts its anti-mitogenic effect by inhibiting mitogen-dependent cyclin D1 induction in the same cells (20). Thus, HMW-HA and LMW-HA have opposing effects on the expression of cyclin D1, and in both cases, the HA effects require CD44 (this study and Refs. 19,20). These results emphasize how a seemingly subtle change in matrix composition, viz. the size of HA, can result in qualitatively distinct effects on cell proliferation. Recent studies indicate that CD44 can cooperate with TLR4 in mediating its signals (39), but consistent with other reports (40,41), we were unable to detect TLR4 surface expression by flow cytometry in unstimulated human vascular smooth muscle cells or following treatment with either HMW-HA or LMW-HA, nor did exposure to HMW-HA or LMW-HA have any pronounced effect on the surface levels of CD44 (data not shown).
The ERK-MAPK pathway and the Rac pathway stimulate cyclin D1 gene expression in several cell types, and we show here that both pathways contribute to the mitogen-dependent induction of cyclin D1 mRNA in human vascular SMCs. ERK and Rac activities are stimulated in response to several receptor types, including receptor tyrosine kinases, integrins, and cadherins. Although signaling by CD44 is not well understood, the mechanisms by which CD44 is thought to signal (e.g. regulation of ERM proteins and release of its intracellular domain) are different from the canonical mechanisms associated with activation of receptor tyrosine kinases, integrins, and cadherins. Thus, it appears that a very divergent set of receptor-proximal signaling mechanisms converge to regulate the activation of ERKs and Rac.
One of the salient findings in this study is that the opposing effects of HMW-HA and LMW-HA on cyclin D1 gene expres- Quiescent human vascular SMCs were stimulated with 10% FBS in the presence or absence of HMW-HA for 24 h. A, total RNA was isolated and used to determine levels of cyclin D1 mRNA and 18 S rRNA by QPCR. The expression of cyclin D1 mRNA was normalized to 18 S rRNA. B, cells simulated for 0 -9 h were collected, lysed, and immunoblotted for dually phosphorylated ERK1/2 (pERK1/2) and ERK2 (loading control). The blot (left) shows a representative result, and the graph (right) plots the ratio of activated ERK/total ERK as fold stimulation (mean Ϯ S.D.) relative to serum-starved cells as determined from densitometric analysis of immunoblots from three independent experiments using ImageJ. Two-tailed t tests showed that the slight reduction in pERK/ERK levels in control and HMW-HA treated cells at 9 h was not statistically significant (p ϭ 0.43). C, quiescent human vascular SMCs were pretreated with HMW-HA or LMW-HA before being incubated with 10% FBS or 0.1% FBS, respectively, for 3 and 5 min. Cells were collected and lysed, and Rac activity was determined using a Rac G-LISA kit. Standard deviations in C ranged from 0.06 to 0.38. sion result from differential regulation of the ERK and Rac pathways to cyclin D1 mRNA (Fig. 7). Specifically, the inhibitory effect of HMW-HA results from inhibition of mitogen-dependent Rac-GTP loading (hence Rac signaling to cyclin D1), whereas the stimulatory effect of LMW-HA results from a strong enhancement of ERK activation in response to weak mitogenic stimulation. We do not yet understand how the binding of HMW-HA versus LMW-HA to CD44 differentially regulate the ERK and Rac pathways, but we speculate that it might involve differential association of ERM proteins or merlin (which have been linked to ERK and Rac) as well as differential binding of CD44 to a receptor tyrosine kinase or integrin (42,43). Consistent with the latter notion, we note that others have attributed the effect of CD44 on platelet-derived growth factor responsiveness to an association between CD44 and the plateletderived growth factor-BB receptor (44,45).
In vivo, HA exists in a dynamic balance between its native, high molecular weight form and lower molecular weights forms that arise from the action of specific hyaluronidases or from oxidative degradation (see Introduction). The degradation of HMW-HA to LMW-HA occurs at sites of inflammation (as seen in atherosclerosis, rheumatoid arthritis, and tumorigenesis), and we have previously proposed (20) that this dynamic balance allows HA and CD44 to act as a proliferation rheostat. First, decreases in the levels of HMW-HA would reduce the anti-mitogenic effect of CD44. Second, the consequent increase in the levels of LMW-HA would enhance the pro-mitogenic effect of CD44. This study explains that the rheostat works by differentially regulating the ERK and Rac signaling pathways to a common cell cycle target, cyclin D1 (Fig. 7). Although our work has focused on cell cycle progression, it is noteworthy that ERK and Rac regulate cell migration and apoptosis as well as proliferation (46,47). Differential coupling of CD44 to ERK and Rac by HMW-and LMW-HA may therefore have widespread implications for the physiological and pathological effects of CD44.


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Molecular Basis for the Sorting of the SNARE VAMP7 into Endocytic Clathrin-Coated Vesicles by the ArfGAP Hrb

Summary SNAREs provide the specificity and energy for the fusion of vesicles with their target membrane, but how they are sorted into the appropriate vesicles on post-Golgi trafficking pathways is largely unknown. We demonstrate that the clathrin-mediated endocytosis of the SNARE VAMP7 is directly mediated by Hrb, a clathrin adaptor and ArfGAP. Hrb wraps 20 residues of its unstructured C-terminal tail around the folded VAMP7 longin domain, demonstrating that unstructured regions of clathrin adaptors can select cargo. Disrupting this interaction by mutation of the VAMP7 longin domain or depletion of Hrb causes VAMP7 to accumulate on the cell's surface. However, the SNARE helix of VAMP7 binds back onto its longin domain, outcompeting Hrb for binding to the same groove and suggesting that Hrb-mediated endocytosis of VAMP7 occurs only when VAMP7 is incorporated into a cis-SNARE complex. These results elucidate the mechanism of retrieval of a postfusion SNARE complex in clathrin-coated vesicles.

NRK cells were transfected using Fugene 6 transfection reagent (Roche Diagnostics Ltd.) at a ratio of 2µg DNA: 6 µl Fugene. NRK cells were transfected with ∆pMEP4-VAMP7 constructs and stable cell lines generated by growing cells in DMEM supplemented with 0.2 mg/ml hygromycin (Roche Diagnostics Ltd.).
∆pMEP4 contains a metallothionein promoter, hence expression of VAMP7-HA (stable cell lines) or CD8 constructs (transients) was induced by adding 10 µM CdCl 2 for 16 h to the tissue culture medium before experiments, NB treatment with cadmium results in some variation of expression levels in individual cells. In the case of CD8 constructs 21µM leupeptin was added with the CdCl 2 to inhibit lysosomal proteases.
For depletion of clathrin a single oligonucleotide chc-2 as described in (Motley et al., 2003) was used. There is a single mis-match between this oligonucleotide and the rat sequence, but after a single transfection of siRNA at 10nM there was a decrease in clathrin heavy chain levels (Supplementary Figure S4).
The corresponding anti-sense sequences were phosphorylated at their 5' end.
A single dsRNA oligonucleotide was also designed to human Hrb (used in EGF assays), the sense sequence was 5'-AGUCGUGGCAUCAGUUCAUdTdT-3'. The TSG101 and AMSH siRNA duplexes were as described (Garrus et al., 2001;McCullough et al., 2004). All dsRNA oligonucleotides were purchased from Dharmacon. RNAi experiments were performed using oligofectamine and dsRNA oligonucleotides at 100 nM.

Iodination of monoclonal anti-HA and binding studies.
20µg of purified monoclonal anti-HA (clone HA.11; Covance) was iodinated for 10 min using 125 I by the Iodogen method (Pierce) and free iodide removed by passage through an Econo-Pac 10DG desalting column (Bio-Rad). A typical iodination resulted in a specific activity of ~5,000 Ci/mmole. For binding studies, NRK cells cells stably expressing VAMP7-HA, and un-transfected NRK cells, were fixed with 4% paraformaldehyde for 20 min at 21˚C. For total binding, half of the cells for each condition were permeabilised with 0.1% (v/v) Triton X-100 for 10 min at 21˚C. All cells were then incubated in 0.2% (w/v) BSA in PBS for 1 h at 37˚C.
Cells were then incubated with the iodinated antibody (50 µCi/ml; 0.3ml/well of a 24 well dish) in PBS supplemented with 0.2% (w/v) BSA, for 1 h at 21˚C. Unbound antibody was removed by washing cells 8X with 1 ml of the PBS-BSA solution. Cells were solubilised with 200 µl of 0.2M NaOH, and the radioactivity counted. Total protein/well was also measured using the BCA protein assay (Pierce). Calculations of the % cell surface VAMP7-HA was determined by dividing the counts/µg protein for non-permeabilised cells by the counts/µg protein for permeabilised cells for each condition. Background counts were determined by using un-transfected NRK cells and background surface and total counts were taken into account in all calculations.

EGF uptake and degradation assays.
Epidermal growth factor (EGF) uptake and degradation was performed as previously described (Bowers et al., 2006). The graph in Supplementary Figure S4 represent counts in the TCA supernatants (i.e. degraded 125 I-EGF) per µg of protein in each sample as a percentage of the total counts bound at time zero (also per µg of protein).
The structure was determined by SIRAS using one native and one mercury derivative data as molecular replacement techniques failed. Seven mercury sites were identified in the derivative using autoSHARP (Vonrhein et al., 2006), and phasing was subsequently undertaken with SHARP (Bricogne et al., 2003). Although the derivative diffracted to 2.5 Å, little phase information was obtained beyond 5 Å resolution. Solvent flattening using SOLOMON (Abrahams and Leslie, 1996) and DM (Cowtan and Main, 1996) was carried out in SHARP. Despite the poor quality of the initial phases, the map after solvent flattening was interpretable and could be used for model building (see Supplementary Figure S2). SHARP optimized the solvent content at 54.6%, with three copies in the asymmetric unit; these molecules were named chains 1, 2, and 3.
Initial model building was carried out in O (Jones et al., 1991), while COOT (Emsley and Cowtan, 2004) was employed for rebuilding. After several rounds of refinement using REFMAC5, it became clear in the F O -F C map that the asymmetric unit contained four copies instead of three. Large patches of "missing" density were 5 observed, indicating density as yet unaccounted for in the model. These patches suggested the presence of β-sheet structure. PHASER (McCoy et al., 2007) was then employed successfully to locate the fourth copy, called chain 4. Molecule 1 was used as the search model, and the positions of molecules 1, 2, and 3 were listed as "known" in the input file. Early rounds of refinement were undertaken using Hendrickson-Lattman coefficients obtained from SHARP, while late rounds employed no prior phase information. The structure was refined to final R cryst and R free values of 22.8% and 28.6% respectively with no residues in disallowed regions (Davis et al., 2007). interacting residues conserved between the two proteins (see Supplementary Figure   9A). HrbL is able to localise to clathrin coated pits/vesicles as shown in Supplementary Figure 9B and Figure 5A). (1/20 th of total lysate) and Hrb isolated on the Sepharose beads after the pulldown.     Depletion of TSG101 and AMSH were used as controls because these respectively cause a reduction and increase in the rate of EGF degradation (Bowers et al., 2006).   Hrb simultaneously binds VAMP7 and AP2 appendage domains causing VAMP7 to be internalised as cargo by clathrin mediated endocytosis. Following delivery to the recycling endosome, VAMP7 can then be sorted by AP3 to its major intracellular location on late endocytic organelles. The SNARE complex in which VAMP7 was participating during its endocytosis must be disassembled and the other SNAREs to which it was bound recycled back to the cell's limiting membrane EH-domain binding NPF motifs are indicated by an asterisk. Identical residues between the two proteins are shaded black, conserved residues grey. The alignment was performed using ClustalW (http://www.ebi.ac.uk/clustalw/), using default parameters.